Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
Authors: Kaidi Cao, Jiaxuan You, Jure Leskovec
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Meta Link on 6 benchmark datasets in both biochemical and vision domains. Experiments demonstrate that Meta Link can successfully utilize the relations among different tasks, outperforming the state-of-the-art methods under the proposed relational multi-task learning setting, with up to 27% improvement in ROC AUC. |
| Researcher Affiliation | Academia | Kaidi Cao Jiaxuan You Jure Leskovec Department of Computer Science, Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Meta Link Training in Relational Meta Setting |
| Open Source Code | Yes | Source code is available at https://github.com/snap-stanford/Graph Gym |
| Open Datasets | Yes | Tox21 (Huang et al., 2016), Sider (Kuhn et al., 2016), Tox Cast (Richard et al., 2016), and MS-COCO (Lin et al., 2014) |
| Dataset Splits | Yes | We search over the number of layers of [2, 3, 4, 5], and report the test set performance when the best validation set performance is reached. |
| Hardware Specification | Yes | We use one NVIDIA RTX 8000 GPU for each experiment and the most time-consuming one (MS-COCO) takes less than 24 hours. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number for it or other software dependencies. |
| Experiment Setup | Yes | We use Adam optimizer, with initial learning of 0.001 and cosine learning rate scheduler. The model is trained with a batch size of 128 for 50 epochs. |